Optimizing Adaptive Monitoring in Resource Constrained Environments

ABSTRACT

Adaptive monitoring dynamically optimizes the monitoring frequency of metrics with respect to system constraints. One or more metrics are monitored. The monitoring includes receiving a value for the metric and evaluating the received metric value. If the evaluation is determined to affect one or monitoring parameters, or if an environment-based event occurs the metrics are adapted. Adapting metrics includes removing or adding a metric based on each metric&#39;s correlation to the affected monitoring parameter or environment based trigger. The frequencies of the metrics are optimized based on the available resources.

BACKGROUND

The present embodiments relate to adaptive monitoring. Morespecifically, the embodiments relate to dynamically optimizing thefrequency of metrics and types of metrics with respect to systemconstraints.

Computer systems monitor various metrics, e.g. physical properties,utilizing various sensors and devices. A sensor is a device that detectsor measures a property, and records, indicates, transmits, or respondsto the detection or measurement. A metric can be weather related (e.g.temperature and humidity), health related (e.g. blood pressure and heartbeat), transportation related (traffics accidents and traffic jams), orother types of metrics. Various sensor configurations may be employed tomeasure different metrics.

It is understood that sensors and associated sensor data related to themetrics are employed in a system to support the operation of one or morephysical devices. Physical devices operate more effectively whensupported by the appropriate metrics and appropriate frequency ofreceipt of sensor data correlated to the metrics. An active sensor canmonitor one or more metrics and produce a metric value, e.g. data, totransmit to a hardware device or software. A hardware or software deviceutilizes one or more system resources when determining and processing ametric value. Hardware and software used in the requesting,transmitting, and processing of metric values has a finite quantity ofavailable resources to handle the data. Due to the finite quantity ofavailable resources, all metrics cannot be monitored continuously, andas such are subject to selection.

SUMMARY

A system, computer program product, and method are provided for optimaladaptive monitoring.

In one aspect, a system is provided with a processing unit incommunication with a memory, and a functional unit in communication withthe processing unit. The functional unit has an adaptive monitor toactively monitor at least one metric in real-time. More specifically, afirst metric is monitored at a first frequency returning a first metricvalue, and a secondary condition is passively evaluated. The adaptivemonitor identifies an adaptation of the monitoring based on a condition.The condition may be a first metric value and/or a secondary condition.In response to the condition, the adaptive monitor dynamically adaptsthe monitoring, with the dynamic adjustment including a selection ofmetrics. More specifically, the adaptive monitor assesses selection ofactive metrics to retain, to remove, and to add, in response to thereturned metric value and evaluated secondary condition. The adaptivemonitor computes a frequency for each retained and added metric based onresource availability and selectively assigns the computed frequency tothe added and retained metrics.

In another aspect, a computer program product is provided for optimaladaptive monitoring. The computer program product includes a computerreadable storage medium with embodied program code that is configured tobe executed by a processing unit. Program code to actively monitors atleast one metric in real-time. More specifically, a first metric ismonitored at a first frequency, with the monitoring returning a firstmetric value, and a secondary condition is passively evaluated. Programcode identifies an adaptation of the monitoring based on a condition.The condition may be a first metric value and/or a secondary condition.In response to the condition, program code dynamically adapts themonitoring, with the dynamic adjustment including a selection ofmetrics. More specifically, the dynamic adjustment includes program codeto perform an assessment of a selection of active metrics to retain, toremove, and to add, in response to the returned metric value andevaluated secondary condition. Program code computes a frequency foreach retained and added metric based on resource availability, andselectively assigns the computed frequency to the added and retainedmetrics.

In yet another aspect, a method is provided for optimal adaptation ofmetrics. At least one metric is actively monitored in real-time. Morespecifically, a first metric is monitored at a first frequency returninga first metric value, and a secondary condition is passively evaluated.An adaptation of the monitoring is identified based on a condition, suchas a first metric value and/or a secondary condition. In response to thecondition, monitoring is dynamically adapted by adjustment of aselection of metrics. More specifically, the selection of active metricsassesses and identifies metrics to retain, to remove, and to add, withthe assessment taking into account the returned metric value andevaluated secondary condition. A frequency is computed for each retainedand added metric based on resource availability, and the frequency isselectively assigned to the added and retained metrics.

These and other features and advantages will become apparent from thefollowing detailed description of the presently preferred embodiment(s),taken in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The subject matter which is regarded as embodiments is particularlypointed out and distinctly claimed in the claims at the conclusion ofthe specification. The forgoing and other features, and advantages ofthe embodiments are apparent from the following detailed descriptiontaken in conjunction with the accompanying drawings in which:

FIG. 1 depicts a block diagram illustrating a computer system thatsupports and enables adaptive monitoring.

FIG. 2 depicts a flow chart illustrating a process for optimal adaptivemonitoring.

FIG. 3 is a block diagram illustrating an example of a computersystem/server of a cloud based support system, to implement the processdescribed above with respect to FIGS. 1-2.

FIG. 4 depicts a block diagram illustrating a cloud computerenvironment.

FIG. 5 depicts a block diagram illustrating a set of functionalabstraction model layers provided by the cloud computing environment.

DETAILED DESCRIPTION

It will be readily understood that the components of the presentembodiments, as generally described and illustrated in the Figuresherein, may be arranged and designed in a wide variety of differentconfigurations. Thus, the following detailed description of theembodiments of the apparatus, system, and method of the presentembodiments, as presented in the Figures, is not intended to limit thescope of the embodiments, as claimed, but is merely representative ofselected embodiments.

Reference throughout this specification to “a select embodiment,” “oneembodiment,” or “an embodiment” means that a particular feature,structure, or characteristic described in connection with the embodimentis included in at least one embodiment of the present embodiments. Thus,appearances of the phrases “a select embodiment,” “in one embodiment,”or “in an embodiment” in various places throughout this specificationare not necessarily referring to the same embodiment.

The illustrated embodiments will be best understood by reference to thedrawings, wherein like parts are designated by like numerals throughout.The following description is intended only by way of example, and simplyillustrates certain selected embodiments of devices, systems, andprocesses that are consistent with the embodiments as claimed herein.

A sensor is a device that detects or measures a property (e.g.biological, chemical, and physical), and records, indicates, transmits,or responds to the detection or measurement. Various sensorconfigurations may be employed to measure different properties. It isunderstood that sensors and associated sensor data correlate to one ormore properties may be employed in a system to support one or morephysical devices. Examples of such physical devices may be, but are notlimited to, smart phones and smart cars. Data gathered from the sensorsis reported to the physical devices and/or an application in support ofthe physical devices. It is noted that the term parameter as describedherein is a monitored metric, and a condition (e.g. secondary condition)is the environmental event that occurs.

Software applications may be, but are not limited to, navigationapplications, health applications and weather applications. In oneembodiment, the sensor data is stored in memory. All systems necessarilyhave a limited capacity with respect to acquiring data and storing data,although some systems may have larger capacity than other systems. Forexample, the amount of data transmitted through a network is limited bybandwidth. Similarly, the amount of stored data is limited by datastorage. Similarly, computer implemented systems that may be employedfor processing the sensor data have limitations related to systemprocessing resources. Comparably, portable devices have limited batterypower.

Performing a selection of sensors, which measure properties thatoptimally support the physical device, is a challenge. Sets of rules areemployed to address and support the property selection and thecorresponding sensor(s) that are employed in the measurement of theproperty. For example, in one embodiment, a first set of rules areemployed to address selection of one or more properties to be measuredand the corresponding sensors to perform the measurement in support ofthe physical device, a second set of rules are employed to addressparameters for delivery of the sensor data from the sensors identifiedwith the first set of rules, e.g. frequency, and a third set of rulesrelate to the physical setting, e.g. environment, of the physical deviceand/or identified sensors. The aspect of attaining the selection isautomated, with the rules setting the selection, frequency, andassociated environment. In one embodiment, attaining the selection isdynamic, with the rules implementing a background process that createsand manages the properties to be measured, the selection and settings ofthe sensors to measure the properties, and application of the sensordata to the physical device in real-time. Accordingly, an optimalconfiguration of the physical device for performing one or more tasks issupported with the dynamic selection and application of the sensors.

Monitoring determines a value for a property (e.g. chemical, biological,and physical). Different categories of monitoring to support thephysical device are employed to address the dynamic aspect of thesupport. One of the categories related to event-based monitoring employsor establishes a set of rules in response to occurrence of an event. Forexample, in the case of a smart automobile performing a navigation taskincluding monitoring a set of metrics of a primary route determines theprimary route is closed, the navigation task monitors a secondary routeincluding metrics of a secondary route to continue the navigation task.In one embodiment, the closed primary route may activate a sensor tomeasure the speed of vehicles on the secondary route. Another categoryof monitoring is referred to as environment-based monitoring, whichemploys or establishes a set of rules in response to a physical devicebeing exposed to a specific scenario. Environment changes are eventsthat trigger the monitoring of other related metrics. For example, ifthe subject device is located in an area where a fire occurs, theselection of sensors and frequency at which the selection is monitoredmay be adapted to address the fire, e.g. activate sensors to measuretemperature, and Carbon Dioxide of the environment surrounding the fire.In one embodiment, in response to the physical device being located inan area subject to a condition, the physical setting of the device maybe modified. Accordingly, rules are employed to address the selection ofsensors based on both event and environmental categories.

Another category of monitoring is referred to as periodic-basedmonitoring which employs or establishes a set of rules for changes incurrently measured properties utilizing sensors to perform measurementsat intervals, e.g. regular time periods, in support of a task on adevice. For example, in the case of a performance of a blood pressuresensor measuring blood pressure once per week, if the value of themeasurement satisfies a monitoring condition, the settings of the sensormay be modified to perform the blood pressure measurement morefrequently, e.g. daily, or less frequently, e.g. monthly. In oneembodiment, a value of a measurement satisfying a monitoring conditioncan affect the selection of parameters to be measured such as adding anadditional parameter, e.g. a measurement of heart rate, or removing aparameter, e.g. stop performance of the blood pressure measurement. Inone embodiment, other periodic measurements may be performed such as atemperature measurement performed once per hour, and a memoryconsumption of physical device performed one time per second.Accordingly, rules are employed to address selection of properties to bemeasured and sensors corresponding to the properties and frequency ofthe measurements based on values for the properties.

Throughout the monitoring process, resources are utilized in support ofthe physical device to request data, read data, transmit data, andprocess data. The resources, utilized in order to measure properties,may be, but are not limited to, network facilities, switches, routers,firewalls, servers, hubs, network access cards, data storage, displayspace, processing resources, electrical power, and battery life. In oneembodiment, the resources have finite capacities which can limit howmuch data can be transmitted, received or processed during a period oftime. In one embodiment, a resource has expandable capacity. Limitingthe resource with expanded capacity to a specific utilization will limitcosts of having to expand the capacity of the resource. Accordingly,increasing measurement of properties (quantity or frequency) increasesresource utilization.

A physical device is in communication with one or more sensors deployedin various locations throughout a network. The physical device utilizesthe one or more sensors to measure a metric in support of a task on thephysical device. Example of the task may be, but are not limited to,navigation, and health monitoring. During performance of the task, datacorresponding to the one or more metrics is utilized. The data thephysical device can receive and process is limited due to resourceconstraints.

The physical device and network have constrained resources which limitthe quantity of data that can be transmitted, received, and processed.For example, during a navigation task, including turn-by-turndirections, a route from a starting point to a destination is selectedincluding a rule to minimize time to destination. The navigation taskincludes monitoring metrics such as traffic flow (e.g. speed,accidents), speed of physical device, and location of the physicaldevice (GPS). The frequency of measurements and any alternative routesthat are monitored are limited due to resource constraints of thephysical device or a network in communication with the physical device.In one embodiment, a traffic accident occurs on the current route, inresponse the physical device changes a physical setting to receivetraffic data corresponding to alternative routes so that the navigationtask can determine if a quicker route is available. In one embodiment,if the measured traffic speed is below a threshold, in response thephysical device changes a physical setting to receive traffic datacorresponding to alternative routes. Accordingly, a physical deviceadapts monitored metrics to optimally perform a task.

An optimal support of the task requires the physical device to beprovided with pertinent metric data. Referring to FIG. 1, a blockdiagram (100) is provided illustrating a computer system that supportsand enables adaptive metric monitoring in support of a task. As shown, aserver, server₀ (120) is shown configured with a processing unit (122)in communication with memory (124) across a bus (126). Server₀ (120) isin communication with a network of shared resources (110) across anetwork connection to provide the server₀ (120) with access to sharedresources, including, but not limited to, shared data resources (170),other servers, such as server₁ (130) and server₂ (140), physicaldevices, such as physical device₀ (150) and physical device₁ (160), andsensors, sensor₀ (112) and sensor₁ (114). In one embodiment, sensor₂(116) is provided in communication with server₀ (120). In oneembodiment, sensor₃ (118) is provided in communication with physicaldevice₁ (160). The position, configuration, and quantity of sensorsshould not be considered limiting. The sensors may be, but are notlimited to, temperature probes, hydrometers, security cameras, GPSsensors, heart beat monitor, blood pressure monitor, or another type ofsensor. Accordingly, a plurality of sensors is shown in communicationwith server₀ (120), either directly or indirectly via the sharedresources (110), with each sensor configured to measure one or moremetrics.

As shown, server₀ (120) is provided with an adaptive monitor (128) tointerface with the sensor(s). The adaptive monitor (108) supports tasksperformed by servers, such as server₀ (120), server₁ (130), server₂(140), physical devices, such as physical device₀ (150) and physicaldevice₁ (160), and other devices. The adaptive monitor (128) is shownherein stored in memory (124), and is in communication with processor(122). In one embodiment, the adaptive monitory (128) is an applicationthat interfaces with the processing unit (122) for execution. Theadaptive monitor (128) has access to sensor₂ (116) through a directconnection, and access to sensor₀ (112) and sensor₁ (114) throughcommunication with the network of shared resources (110), and access tosensor₃ (118) through communication with the network of shared resources(110) and client₁ (164). Accordingly, the adaptive monitor (128) has thecapability to communicate with each sensor in the network, eitherthrough a direct connection or through a network interface.

The adaptive monitor (128) determines the selection of metrics tomeasure and the frequency at which to measure the metrics. Selectingmetrics and determining frequencies for the metrics includesdetermination of the sensor(s) that will perform the measurement of themetric. In one embodiment, the aspect of attaining the selection of ametrics is automated, with rules (102) setting the selection. The rules(102) are embedded in memory (124), in one embodiment in the shared dataresources (170), or in one embodiment, an unshared data resource (notshown). In one embodiment, the rule(s) (102) utilized by the adaptivemonitor (128) corresponding to the task the adaptive monitor (128) issupporting. In one embodiment, attaining the selection is dynamic, withthe adaptive monitor (128) utilizing the rules (102) to implement abackground process that creates and manages the metrics to be measuredand the selection of the sensors to measure the metrics and applicationof the sensor data to a physical device or server in real-time.Accordingly, the adaptive monitor (128) utilizes rules (102) to supportautomatic selection of the metrics to be measured.

A mathematical optimization model (108) is utilized by the adaptivemonitor (128) for computation of the optimal frequency at which tomonitor each metric. In one embodiment, the aspect of attaining theoptimal frequency is automated, with the mathematical optimization model(108) supporting the determination of the optimal frequency for eachmetric. The mathematical optimization model (108) is embedded in memory(124), and in one embodiment, in the shared data resources (170), or inone embodiment, an unshared data resource (not shown). In oneembodiment, the optimization of frequencies is dynamic with the adaptivemonitor (128) utilizing the mathematical optimization model (108) toimplement a background process that determines the frequency at which tocollect the sensor data. Accordingly, the adaptive monitor (128)utilizes the mathematical optimization model (108) to support automaticoptimization of the frequencies at which to collect sensor data.

The adaptive monitor (128) either requests or is configured toautomatically receive data from any one of the sensors in the network.For example, sensor data associated with sensor₀ (112) is communicatedto the adaptive monitor (128) across the network of shared resources(110). The adaptive monitor (128) utilizes processor (122) to convertthe received data into a metric value and stores the metric value inmemory (124). Accordingly, the adaptive monitor (108) monitors a metricby utilization of a sensor.

The adaptive monitor (128) can determine, available resource(s)including capacities and utilization of the resource(s), individualresource utilization by a monitored metric, currently monitoredmetric(s), assigned weight of a metric(s), frequency of the monitoredmetric, and value of each metric. In one embodiment, the assignedweight, list of currently monitored metric(s), and frequency of themonitored metric(s) are stored in memory (124) which can be accessed bythe adaptive monitor (128). In one embodiment, the quantity of metric(s)monitored and frequency of receiving values for those metrics by theadaptive monitor (128) is proportional to the resources utilized in thecomputer system. In one embodiment, the quantity of metrics monitoredand the frequency of measurement of the metrics is proportional toprocessor (124) utilization, bandwidth utilization in the networkconnection provided to server₀ (120), and memory (124) utilization.Accordingly, the adaptive monitor (128) utilizes available systemresources when monitoring a metric.

In one embodiment, a correlation coefficient(s) (104) and a metricweight (106) are stored in memory, such as primary storage location(e.g. rules (102)), and/or a secondary storage location (e.g. shareddata resources (170)). A correlation coefficient (104) is utilized bythe adaptive monitor (128) in order to determine which metric(s) tomeasure and the frequency to measure each metric subject to monitoring.A correlation coefficient (104) connotes relation of (e.g. dependencyof) a metric to another metric and/or an environmental parameter. Ametric weight (106) is a coefficient of a metric corresponding to therelationship of the metric and the task being supported by the metricmonitoring. In one embodiment, the correlation coefficient (104) isused, by the adaptive monitor (128), to determine which metric(s) shouldbe measured and the frequency of measuring each metric. In oneembodiment, the metric weight (106) is used, by the adaptive monitor(128), to determine the frequency at which to measure each metric.Accordingly, a metric weight (106) and correlation coefficient (104) areaccessible to the adaptive monitor (128) for optimal adaptation ofmonitoring.

Optimal adaptive monitoring can be employed in resource constrainedenvironments. Referring to FIG. 2, a flow chart (200) is providedillustrating a process for optimal adaptive monitoring. As shown, ametric is monitored in support of a task on a device by utilizing asensor to perform a measurement of the metric (202). In one embodiment,the metric is initially chosen based on a rule(s) associated with thetask. The device may be, but is not limited to, a physical device, aclient machine, and a server. Monitoring the metric includes a device,or software requesting or collecting sensor data related to a metricvalue(s) at an interval, or in one embodiment, continuously. The aspectof monitoring utilizes available resources in order to process a requestof sensor data, transmit the request, the recipient (e.g. sensor) toreceive and process the request, the recipient to measure the requestedmetric, the recipient to transmit a measurement as a response (e.g.sensor data), and a device or software to receive the sensor data, thedevice or software to process the sensor data, and the device orsoftware to generate a metric value. In one embodiment, two or moremetrics may be the subject of the monitoring shown and described herein.In one embodiment, the metrics may be monitored in parallel. Thequantity of metrics monitored or quantity of sensors subject to themetric measurement(s) should not be considered limiting. Accordingly,one or more metrics are monitored at intervals utilizing availableresources.

During monitoring, sensor data for the monitored metric is received(204), and in one embodiment processed by the device or software. Thereceived metric value is evaluated (206) by the device or software. Thedevice or software utilizes an evaluation rule(s) in order to performthe evaluation. The evaluation rule(s) include comparing the metric(s)value to a threshold including determining whether the metric value isbelow the threshold, equal to the threshold and/or greater than thethreshold. In one embodiment, the evaluation rule(s) include utilizingthe history of sensor data received from a particular sensor todetermine a trend in the metric value, and in one embodiment to comparethe trend to a second threshold. In one embodiment, the evaluationrule(s) include utilizing a deviation of the metric value when comparingthe metric value and/or trend. Accordingly, metric values are receivedfrom a sensor and evaluated based on an evaluation rule.

After the evaluation at step (206), a determination is made of whetherthe evaluation affects one or more monitoring parameters (208) based ona rule. A monitoring parameter includes, but is not limited to, theselection of metrics that are measured and the frequency of performing ameasurement. In one embodiment, the evaluation affects a monitoringparameter when a metric value meets or exceeds a threshold. If thedetermination at step (208) is positive, and the evaluation affects amonitoring parameter, the process proceeds to step (210) to beginoptimal adaptation of the monitoring. If the determination at step (208)is negative, and the evaluation does not affect monitoring parameter(s)the process returns to step (202) and continues to monitor the currentmetric(s) at the current frequency. Accordingly, an affected monitoringparameter indicates the monitoring needs to be optimally adapted.

The device or software determines whether to adapt or otherwise extendthe monitored metric(s) (210). The determination at step (210) includesdetermining whether the metrics currently monitored are the optimalmetrics to be monitored in order to support the task on a physicaldevice. The determination is based on the correlation of the affectedmonitoring parameter to each metric. The correlation may be positive ornegative. In one embodiment, the correlation is based on a correlationcoefficient of a metric having a metric value evaluated to affect themonitoring parameter. If the determination is positive at step (210),the process proceeds to adapt which metrics are monitored (212). In oneembodiment, the determination is positive when a currently monitoredmetric has a negative correlation to the affected monitoring parameter.In one embodiment, the determination is positive when an unmonitoredmetric has a positive correlation to the affected a monitoringparameter. However, if the determination is negative at step (210), theprocess proceeds directly to assigning weight(s) of the metric(s) (214).Accordingly, the affected monitoring parameter is utilized to determinewhether the metric(s) need to be adapted before the metrics areoptimized.

At any time an environmental-based event is triggered (218), with theevent trigger proceeding to step (212) to adapt the metrics. Anenvironmental-based event may be triggered when an event requiresreprioritization of the metrics to be monitored, new metric(s) to bemonitored, or other metric(s) to be removed from monitoring based on anenvironmental rule. Examples of an environment-based event andtriggering of this event includes, but is not limited to, a weathercondition (e.g. a storm), a time of day (e.g. night), a particularlocation (e.g. a city center), a traffic condition (e.g. automobileaccident), a season (e.g. fall), a predefined condition, and a userrequested adaptation. In one embodiment, the environmental-based eventis an auxiliary condition that is pushed to a physical device.Accordingly, the metrics are adapted if either an environment-basedevent occurs or a monitoring parameter is affected.

At step (212), the adaptation of the metric(s) includes adding anadditional metric to be monitored and/or a removal of a metric that isbeing monitored. In one embodiment, the addition or removal of a metricincludes changing a physical setting on the device. Metric(s) are addedand removed based on an adaptation rule including a correlation to theaffected monitoring parameter or environment-based event trigger. Thecorrelation may be based on a correlation coefficient. In oneembodiment, a metric may have a dependency on one or more other metrics.A dependency on one or more metrics connotes the metrics are related,thus the metrics should be added or removed as a group. A negativedependency connotes the metrics are unrelated. In one embodiment, ametric having a negative dependency with the environmental event oraffected monitoring parameter is removed. In one embodiment, anenvironment-based event is associated with an adaptation rule thatincludes a predefined set of metrics that are to be monitored inresponse to the occurrence of the event, thus metrics are added andremoved based on the predefined set of metrics. In one embodiment, whena metric is added having a negative dependency on a currently monitoredmetric, the currently monitored metric is removed. In one embodiment, ifit is raining, a metric to monitor traffic speed is added. In oneembodiment, if blood pressure was measured over a threshold, a metric tomonitor heart rhythm is added. In one embodiment, a first metric havinga negatively dependency is removed and a second metric having a positivedependency on the first metric is also removed unless the second metrichas a secondary positive relationship to a third currently monitoredmetric or a newly added metric. Accordingly, metrics are added andremoved based on their correlation to the environment-based event oraffected monitoring parameter.

Following step (212), weights are assigned to the metrics that arecurrently monitored (and not removed) and any newly added metric(s) tobe monitored (214) by the device or software. In one embodiment, theassignment of weights is stored in memory. In one embodiment, theassignment of weights is based upon an assignment rule. If a monitoringparameter was affected as determined at step (208), the assignment ruleincludes assigning weight(s) based upon current weights of the metric(s)having value(s) that affected a monitoring parameter, other currentlymonitored metric(s) having weight(s), a newly added metric(s) having aweight(s), and the correlation coefficient between metrics. If anenvironment-based event is triggered, the assignment rule includesassigning weight(s) based upon the correlation coefficient between ametric(s) and the environment-based event, and the weight(s) ofcurrently monitored metric(s) including a correlation coefficient of thenewly added metric to the currently monitored metric. Accordingly,weights are assigned to each metric based on assignment rule.

Following assignment of the weights at step (214), the metrics areoptimized for system resource capacity (216). The optimization includeschanging a physical setting on device including changing a frequency ofa measurement and/or assigning a frequency of measurement to a newlyadded metric. In one embodiment, the optimization maximizes frequenciesof metrics with higher weights and minimizes frequencies of metrics withlower weights. After optimization of the metric(s) is performed theprocess returned to step (202) and monitors the active metric(s) at theoptimized frequency(ies). Accordingly, a process is provided in order tooptimally adapt monitoring of metrics in response to a monitoringparameter being affected or an environment-based event.

In one embodiment, the optimization includes offsetting the time ametric is measured from the time another metric is measured. Theoffsetting includes setting the occurrence of the first metricmeasurement from the occurrence of the second metric measurement.Accordingly, offsetting the metrics will allow both metrics to bemeasured at separate times thereby increasing the frequency at whicheach can be monitored while not exceeding available resource capacity.

In one embodiment, the optimal frequency interval of measurement foreach metric from the metric set can be computed in order to maximize thefrequency of the metrics with higher metric weights. In one embodiment,the optimal frequency is computed in logic embedded within memory incommunication with a processor.

A mathematical model is used to compute the optimal frequency intervalof measurement for each metric from the metric set. In order to computethe optimal frequency of measurement, the variables of a mathematicalmodel are defined as follows; I represents the set of metrics that areto be monitored. K represents the set of the frequency intervals formeasurement, ordered from lowest to the highest interval value. Forexample, there are different possible frequency intervals at which ametric can be measured (e.g. measured once every 10 ms, or once every 2minutes). The minimum possible value in set K is the minimum possiblemeasurement interval, k_(min). The maximum possible value in K is themaximum possible measurement interval, k_(max) or |K|, where |K| is thecardinality of the set K. The difference between the values of twosuccessive elements of the set K is equal to the minimum given intervalvalue. J is the set of potential times (e.g. time stamp, particulartime, particular occurrence) at which the measurement can begin. |J| isthe cardinality of the set J, and is equal to k_(max) (|K|). Elements ofJ are ordered and the step size between the elements is chosen where aminimum precision value of the monitoring is achieved. Accordingly, theset J is defined to ensure that the computed optimization will determinethe optimal measurement intervals and times for one complete cycle ofmonitoring.

In the optimization computation, the binary variable X_(ij) is 1 ifmetric, i ∈ I, is to be monitored at time, j ∈ J, otherwise the binaryvariable X_(ij) is 0. The properties of the binary variable X_(ij) arerepresented in the following equation.

X_(ij) ∈ {0,1}, ∀i ∈ I, ⋆j ∈ J

The weight, w_(i), is the weight for metric, I ∈ I. The followingequation is used to maximize the measurement frequency of the metricswith a linear proportion to the weights of each monitored metric.

$\max {\sum\limits_{i \in I}{\sum\limits_{j \in J}{w_{i}*X_{ij}}}}$

The binary variable B_(ij) is 1 if metric, i ∈ I, is to start at time, j∈ J, otherwise the binary variable B_(ij) is 0. The binary variableBA_(ij) is represented by the following equation.

B_(ij) ∈ {0,1}, ∀i ∈ I, ∀j ∈ J

The following equation ensures that each metric begins measurement onceduring a monitoring time cycle.

${{{s.t.\mspace{14mu} {\sum\limits_{j \in J}B_{ij}}} = 1},{\forall{i \in I}}}\;$

The binary variable Interval_(ki) is 1 if metric, i ∈ I, is monitored atfrequency k ∈ K otherwise the binary variable Interval_(ki) is 0. Thebinary variable Interval_(ki) is represented by the following equation.

Interval_(ki) ∈ {0,1}, ∀k ∈ K, ∀i ∈ I

The following equation ensures each metric is assigned only oneinterval.

${{\sum\limits_{k \in K}{Interval}_{ki}} = 1},{\forall{i \in I}}$

The relationship between X_(ij) and B_(if) is represented in thefollowing equation which sets the first monitored time to 1.

X_(ij)≥B_(ij), ∀i ∈ I, ∀j ∈ J

The relationship between X_(ij) and B_(ij) is further defined in thefollowing equation which sets all the monitoring before the firstmeasurement for each metric, i ∈ I, to zero.

X _(ij)≤(1−B _(ij)), ∀l ∈ {1, . . . , j−1}

X_(ij) and B_(ij) ensure that measurement of each metric will begin at aspecific chosen time. The following equation ensures that the subsequentmeasurements are at a similar interval for each metric.

${{\sum\limits_{z \in {\{{j,\; \ldots \;,\; {j + k - 1}}\}}}X_{ij}} \geq {Interval}_{ki}},{\forall{i \in I}},{\forall{j \in {J\text{:}\left( {j + k - 1} \right)} \in J}},{\forall{K \in K}}$

The set of resources used for the metric monitoring is defined as R.Each resource, r ∈ R, has a have a given capacity, rcap_(rj), at time, j∈ J. Each metric, i ∈ I, utilizes an amount, ut_(ir), of resource, r ∈R, when the metric, i ∈ I, is being monitored at time, j ∈ J. Thefollowing equation ensures that at each time, j ∈ J, the total resourceutilization of each metric, i ∈ I, monitored is less than or equal tothe available resources at that time for each resource, r ∈ R.

${{\sum\limits_{i \in I}{{ut}_{ir}*X_{ij}}} \leq {rcap}_{jr}},{\forall{j \in J}},{\forall{r \in R}}$

Computing the preceding equations optimizes the measurement intervalsand times for one complete monitoring cycle. The equations can berecomputed for each monitoring cycle. In one embodiment, computing andsolving the preceding equations guarantees an optimal solution (e.g.optimal frequency and optimal start time) which maximizes the frequencyof the monitored metrics while limiting use of available resources.Accordingly, the frequency at which to measure a metric can be computedin order to optimally monitor metrics which utilize constrainedresources.

Aspects of adapting monitoring provided in FIGS. 1-2, employ one or morefunctional tools to support use of adaptive monitoring. Aspects of thefunctional tool, e.g. adaptive monitor, and its associated functionalitymay be embodied in a computer system/server in a single location, or inone embodiment, may be configured in a cloud based system sharingcomputing resources. With references to FIG. 3, a block diagram (300) isprovided illustrating an example of a computer system/server (302),hereinafter referred to as a host (302) in communication with a cloudbased support system, to implement the processes described above withrespect to FIGS. 1-2. Host (302) is operational with numerous othergeneral purpose or special purpose computing system environments orconfigurations. Examples of well-known computing systems, environments,and/or configurations that may be suitable for use with host (302)include, but are not limited to, personal computer systems, servercomputer systems, thin clients, thick clients, hand-held or laptopdevices, multiprocessor systems, microprocessor-based systems, set topboxes, programmable consumer electronics, network PCs, minicomputersystems, mainframe computer systems, and file systems (e.g., distributedstorage environments and distributed cloud computing environments) thatinclude any of the above systems, devices, and their equivalents.

Host (302) may be described in the general context of computersystem-executable instructions, such as program modules, being executedby a computer system. Generally, program modules may include routines,programs, objects, components, logic, data structures, and so on thatperform particular tasks or implement particular abstract data types.Host (302) may be practiced in distributed cloud computing environmentswhere tasks are performed by remote processing devices that are linkedthrough a communications network. In a distributed cloud computingenvironment, program modules may be located in both local and remotecomputer system storage media including memory storage devices.

As shown in FIG. 3, host (302) is shown in the form of a general-purposecomputing device. The components of host (302) may include, but are notlimited to, one or more processors or processing units (304), a systemmemory (306), and a bus (308) that couples various system componentsincluding system memory (306) to processor (304). Bus (308) representsone or more of any of several types of bus structures, including amemory bus or memory controller, a peripheral bus, an acceleratedgraphics port, and a processor or local bus using any of a variety ofbus architectures. By way of example, and not limitation, sucharchitectures include Industry Standard Architecture (ISA) bus, MicroChannel Architecture (MCA) bus, Enhanced ISA (EISA) bus, VideoElectronics Standards Association (VESA) local bus, and PeripheralComponent Interconnects (PCI) bus. Host (302) typically includes avariety of computer system readable media. Such media may be anyavailable media that is accessible by host (302) and it includes bothvolatile and non-volatile media, removable and non-removable media.

Memory (306) can include computer system readable media in the form ofvolatile memory, such as random access memory (RAM) (330) and/or cachememory (332). By way of example only, storage system (334) can beprovided for reading from and writing to a non-removable, non-volatilemagnetic media (not shown and typically called a “hard drive”). Althoughnot shown, a magnetic disk drive for reading from and writing to aremovable, non-volatile magnetic disk (e.g., a “floppy disk”), and anoptical disk drive for reading from or writing to a removable,non-volatile optical disk such as a CD-ROM, DVD-ROM or other opticalmedia can be provided. In such instances, each can be connected to bus(308) by one or more data media interfaces.

Program/utility (340), having a set (at least one) of program modules(342), may be stored in memory (306) by way of example, and notlimitation, as well as an operating system, one or more applicationprograms, other program modules, and program data. Each of the operatingsystems, one or more application programs, other program modules, andprogram data or some combination thereof, may include an implementationof a networking environment. Program modules (342) generally carry outthe functions and/or methodologies of embodiments to store and analyzedata. For example, the set of program modules (342) may include themodules configured for adaptive monitoring as described in FIGS. 1 and3.

Host (302) may also communicate with one or more external devices (314),such as a keyboard, a pointing device, etc.; a display (324); one ormore devices that enable a user to interact with host (302); and/or anydevices (e.g., network card, modem, etc.) that enable host (302) tocommunicate with one or more other computing devices. Such communicationcan occur via Input/Output (I/O) interface(s) (322). Still yet, host(302) can communicate with one or more networks such as a local areanetwork (LAN), a general wide area network (WAN), and/or a publicnetwork (e.g., the Internet) via network adapter (320). As depicted,network adapter (320) communicates with the other components of host(302) via bus (308). In one embodiment, a plurality of nodes of adistributed file system (not shown) is in communication with the host(302) via the I/O interface (322) or via the network adapter (320). Itshould be understood that although not shown, other hardware and/orsoftware components could be used in conjunction with host (302).Examples, include, but are not limited to: microcode, device drivers,redundant processing units, external disk drive arrays, RAID systems,tape drives, and data archival storage systems, etc.

In this document, the terms “computer program medium,” “computer usablemedium,” and “computer readable medium” are used to generally refer tomedia such as main memory (306), including RAM (330), cache (332), andstorage system (334), such as a removable storage drive and a hard diskinstalled in a hard disk drive.

Computer programs (also called computer control logic) are stored inmemory (306). Computer programs may also be received via a communicationinterface, such as network adapter (320). Such computer programs, whenrun, enable the computer system to perform the features of the presentembodiments as discussed herein. In particular, the computer programs,when run, enable the processing unit (304) to perform the features ofthe computer system. Accordingly, such computer programs representcontrollers of the computer system.

The present embodiments may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent embodiments.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present embodiments may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present embodiments.

In one embodiment, host (302) is a node of a cloud computingenvironment. As is known in the art, cloud computing is a model ofservice delivery for enabling convenient, on-demand network access to ashared pool of configurable computing resources (e.g., networks, networkbandwidth, servers, processing, memory, storage, applications, virtualmachines, and services) that can be rapidly provisioned and releasedwith minimal management effort or interaction with a provider of theservice. This cloud model may include at least five characteristics, atleast three service models, and at least four deployment models. Exampleof such characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based email). Theconsumer does not manage or control the underlying cloud infrastructureincluding network, servers, operating systems, storage, or evenindividual application capabilities, with the possible exception oflimited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting for loadbalancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 4, an illustrative cloud computing network (400).As shown, cloud computing network (400) includes a cloud computingenvironment (450) having one or more cloud computing nodes (410) withwhich local computing devices used by cloud consumers may communicate.Examples of these local computing devices include, but are not limitedto, personal digital assistant (PDA) or cellular telephone (454A),desktop computer (454B), laptop computer (454C), and/or automobilecomputer system (454N). Individual nodes within nodes (410) may furthercommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment (400) to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices (454A-N)shown in FIG. 4 are intended to be illustrative only and that the cloudcomputing environment (450) can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 5, a set of functional abstraction layers providedby the cloud computing network of FIG. 3 is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 5 are intended to be illustrative only, and the embodiments arenot limited thereto. As depicted, the following layers and correspondingfunctions are provided: hardware and software layer (510),virtualization layer (520), management layer (530), and workload layer(540). The hardware and software layer (510) includes hardware andsoftware components. Examples of hardware components include mainframes,in one example IBM® zSeries® systems; RISC (Reduced Instruction SetComputer) architecture based servers, in one example IBM pSeries®systems; IBM xSeries® systems; IBM BladeCenter® systems; storagedevices; networks and networking components. Examples of softwarecomponents include network application server software, in one exampleIBM WebSphere® application server software; and database software, inone example IBM DB2® database software. (IBM, zSeries, pSeries, xSeries,BladeCenter, WebSphere, and DB2 are trademarks of International BusinessMachines Corporation registered in many jurisdictions worldwide).

Virtualization layer (520) provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers;virtual storage; virtual networks, including virtual private networks;virtual applications and operating systems; and virtual clients.

In one example, management layer (530) may provide the followingfunctions: resource provisioning, metering and pricing, user portal,service level management, and SLA planning and fulfillment. Resourceprovisioning provides dynamic procurement of computing resources andother resources that are utilized to perform tasks within the cloudcomputing environment. Metering and pricing provides cost tracking asresources are utilized within the cloud computing environment, andbilling or invoicing for consumption of these resources. In one example,these resources may comprise application software licenses. Securityprovides identity verification for cloud consumers and tasks, as well asprotection for data and other resources. User portal provides access tothe cloud computing environment for consumers and system administrators.Service level management provides cloud computing resource allocationand management such that required service levels are met. Service LevelAgreement (SLA) planning and fulfillment provides pre-arrangement for,and procurement of, cloud computing resources for which a futurerequirement is anticipated in accordance with an SLA.

Workloads layer (540) provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include, but are notlimited to: mapping and navigation; software development and lifecyclemanagement; virtual classroom education delivery; data analyticsprocessing; transaction processing; and optimal adaptive monitoring.

As will be appreciated by one skilled in the art, the aspects may beembodied as a system, method, or computer program product. Accordingly,the aspects may take the form of an entirely hardware embodiment, anentirely software embodiment (including firmware, resident software,micro-code, etc.), or an embodiment combining software and hardwareaspects that may all generally be referred to herein as a “circuit,”“module,” or “system.” Furthermore, the aspects described herein maytake the form of a computer program product embodied in one or morecomputer readable medium(s) having computer readable program codeembodied thereon.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device.

The embodiments are described above with reference to flow chartillustrations and/or block diagrams of methods, apparatus (systems), andcomputer program products. It will be understood that each block of theflow chart illustrations and/or block diagrams, and combinations ofblocks in the flow chart illustrations and/or block diagrams, can beimplemented by computer program instructions. These computer programinstructions may be provided to a processor of a general purposecomputer, special purpose computer, or other programmable dataprocessing apparatus to produce a machine, such that the instructions,which execute via the processor of the computer or other programmabledata processing apparatus, create means for implementing thefunctions/acts specified in the flow chart and/or block diagram block orblocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flow chart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions, which execute on thecomputer or other programmable apparatus, provide processes forimplementing the functions/acts specified in the flow chart and/or blockdiagram block or blocks.

The flow charts and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments. In this regard, each block in the flow charts or blockdiagrams may represent a module, segment, or portion of code, whichcomprises one or more executable instructions for implementing thespecified logical function(s). It should also be noted that, in somealternative implementations, the functions noted in the block may occurout of the order noted in the figures. For example, two blocks shown insuccession may, in fact, be executed substantially concurrently, or theblocks may sometimes be executed in the reverse order, depending uponthe functionality involved. It will also be noted that each block of theblock diagrams and/or flow chart illustration(s), and combinations ofblocks in the block diagrams and/or flow chart illustration(s), can beimplemented by special purpose hardware-based systems that perform thespecified functions or acts, or combinations of special purpose hardwareand computer instructions.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting. As used herein, thesingular forms “a”, “an” and “the” are intended to include the pluralforms as well, unless the context clearly indicates otherwise. It willbe further understood that the terms “comprises” and/or “comprising,”when used in this specification, specify the presence of statedfeatures, integers, steps, operations, elements, and/or components, butdo not preclude the presence or addition of one or more other features,integers, steps, operations, elements, components, and/or groupsthereof.

The embodiments described herein may be implemented in a system, amethod, and/or a computer program product. The computer program productmay include a computer readable storage medium (or media) havingcomputer readable program instructions thereon for causing a processorto carry out the embodiments described herein.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmissions, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

The embodiments are described herein with reference to flow chartillustrations and/or block diagrams of methods, apparatus (systems), andcomputer program products. It will be understood that each block of theflow chart illustrations and/or block diagrams, and combinations ofblocks in the flow chart illustrations and/or block diagrams, can beimplemented by computer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flow chart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flow chart and/or block diagram blockor blocks.

It will be appreciated that, although specific embodiments have beendescribed herein for purposes of illustration, various modifications maybe made without departing from the spirit and scope of the specificembodiments described herein. Accordingly, the scope of protection islimited only by the following claims and their equivalents.

Aspects of the present embodiments are described herein with referenceto flowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments. Itwill be understood that each block of the flowchart illustrations and/orblock diagrams, and combinations of blocks in the flowchartillustrations and/or block diagrams, can be implemented by computerreadable program instructions.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription of the present embodiments has been presented for purposesof illustration and description, but is not intended to be exhaustive orlimited to the embodiments in the form disclosed.

Indeed, executable code could be a single instruction, or manyinstructions, and may even be distributed over several different codesegments, among different applications, and across several memorydevices. Similarly, operational data may be identified and illustratedherein within the tool, and may be embodied in any suitable form andorganized within any suitable type of data structure. The operationaldata may be collected as a single dataset, or may be distributed overdifferent locations including over different storage devices, and mayexist, at least partially, as electronic signals on a system or network.

Furthermore, the described features, structures, or characteristics maybe combined in any suitable manner in one or more embodiments. In thefollowing description, numerous specific details are provided, such asexamples of agents, to provide a thorough understanding of the disclosedembodiments. One skilled in the relevant art will recognize, however,that the embodiments can be practiced without one or more of thespecific details, or with other methods, components, materials, etc. Inother instances, well-known structures, materials, or operations are notshown or described in detail to avoid obscuring aspects of theembodiments.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription of the present embodiments have been presented for purposesof illustration and description, but is not intended to be exhaustive orlimited to the embodiments in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the embodiments. Theembodiment was chosen and described in order to best explain theprinciples of the embodiments and the practical application, and toenable others of ordinary skill in the art to understand the embodimentsfor various embodiments with various modifications as are suited to theparticular use contemplated. Accordingly, the implementation adaptivemonitoring maximizes the monitoring frequency of metrics with greaterimportance.

It will be appreciated that, although specific embodiments have beendescribed herein for purposes of illustration, various modifications maybe made without departing from the spirit and scope of the embodiments.In particular, a sensor can be monitored remotely by any deviceconnected to the cloud computing network. Additionally, resourceconstraints are not limited to the device actually performing themonitoring. Accordingly, the scope of protection of these embodiments islimited only by the following claims and their equivalents.

What is claimed is:
 1. A computer system comprising: a memory; aprocessor, communicatively coupled to the memory; a functional unit incommunication with the processing unit having an adaptive monitor, theadaptive monitor to: actively monitor, in real-time, at least onemetric, including a first metric at a first frequency returning a firstmetric value, and passively evaluate a secondary condition; identify anadaptation of the monitoring based on a condition selected from thegroup consisting of: the first metric value, the secondary condition,and a combination thereof; dynamically adapt the monitoring responsiveto the condition, the dynamic adaptation comprising the adaptive monitorto: adjust a selection of metrics, including assess selection of activemetrics to retain, assess selection of active metrics to remove, andassess selection of metrics to add, the selections responsive to thereturned metric value and evaluated secondary condition; compute afrequency for each retained and added metric based on resourceavailability; and selectively assign the computed frequency to the addedand retained metrics.
 2. The system of claim 1, further comprising theadaptive monitor to compute a weight for each retained and added metricand selectively assign the computed weight to the added and retainedmetrics, wherein the computation of the frequency is based on theselectively assigned weight.
 3. The system of claim 2, wherein theweight assignment individually influences a strength of a single metricwithin a grouping of active metrics.
 4. The system of claim 2, furthercomprising the adaptive monitor to selectively maximize a monitoringfrequency for each added and retained metric, the frequency based on thecomputed weight and resource availability.
 5. The system of claim 2,wherein the computed weight for each retained and added metriccorresponds to a correlation coefficient between at least two metricsselected from the group consisting of: at least one retained metric andat least one added metric.
 6. The system of claim 1, wherein computationof the frequency includes the adaptive monitor to compute an optimalfrequency interval and an optimal frequency interval start time.
 7. Thesystem of claim 1, wherein the adaptive monitor is provided as a servicein a cloud environment to perform the dynamic adaptation.
 8. A computerproduct for optimal adaptive monitoring, the computer program productcomprising a computer readable storage medium having program codeembodied therewith, the program code executable by a processor to:actively monitor, in real-time, at least one metric, including a firstmetric at a first frequency returning a first metric value, andpassively evaluate a secondary condition; identify an adaptation of themonitoring based on a condition selected from the group consisting of:the first metric value, the secondary condition, and a combinationthereof; dynamically adapt the monitoring responsive to the condition,the dynamic adaptation comprising the adaptive monitor to: adjust aselection of metrics, including assess selection of active metrics toretain, assess selection of active metrics to remove, and assessselection of metrics to add, the selections responsive to the returnedmetric value and evaluated secondary condition; compute a frequency foreach retained and added metric based on resource availability; andselectively assign the computed frequency to the added and retainedmetrics.
 9. The computer program product of claim 8, further comprisingprogram code to compute a weight for each retained and added metric andselectively assign the computed weight to the added and retained metricswherein computation of the frequency is based on the selectivelyassigned weight.
 10. The computer program product of claim 9, whereinthe weight assignment individually influences a strength of a singlemetric within a grouping of active metrics.
 11. The computer programproduct of claim 9, further comprising program code to selectivelymaximize a monitoring frequency for each added and retained metric, thefrequency based on the computed weight and resource availability. 12.The computer program product of claim 9, wherein the computed weight foreach retained and added metric corresponds to a correlation coefficientbetween at least two metrics selected from the group consisting of: atleast one retained metric and at least one added metric.
 13. Thecomputer program product of claim 8, wherein computation of thefrequency includes program code to compute an optimal frequency intervaland an optimal frequency interval start time.
 14. A method for optimaladaptive monitoring, the method comprising: actively monitoring, inreal-time, at least one metric, including a first metric at a firstfrequency returning a first metric value, and passively evaluating asecondary condition; identifying an adaptation of the monitoring basedon a condition selected from the group consisting of: the first metricvalue, the secondary condition, and a combination thereof; dynamicallyadapting the monitoring responsive to the condition, the dynamicadaptation comprising: adjusting a selection of metrics, includingassessing selection of active metrics to retain, assessing selection ofactive metrics to remove, and assessing selection of metrics to add, theselections responsive to the returned metric value and evaluatedsecondary condition; computing a frequency for each retained and addedmetric based on resource availability; and selectively assigning thecomputed frequency to the added and retained metrics.
 15. The method ofclaim 14, further comprising computing a weight for each retained andadded metric and selectively assigning the computed weight to the addedand retained metrics wherein computing the frequency is based on theselectively assigned weight.
 16. The method of claim 15, wherein theweight assignment individually influences a strength of a single metricwithin a grouping of active metrics.
 17. The method of claim 15, furthercomprising selectively maximizing a monitoring frequency for each addedand retained metric, the frequency based on the computed weight andresource availability.
 18. The method of claim 15, wherein the computedweight for each retained and added metric corresponds to a correlationcoefficient between at least two metrics selected from the groupconsisting of: at least one retained metric and at least one addedmetric.
 19. The method of claim 14, wherein computing the frequencyincludes computing an optimal frequency interval and an optimalfrequency interval start time.
 20. The method of claim 14, whereinsoftware is provided as a service in a cloud environment to perform thedynamic adaptation.